Operational Data Model

Operational Data Model (ODM). The ODM is a powerful XML-based data model that allows for XMF-based transmission of any data involved in the conduct of clinical trials. SAS has provided support for importing and exporting ODM files via the CDISC procedure and the XML LIBNAME engine. 

Operational Data Model (ODM) for clinical data interchange... 

It is a daunting task to put together overall steam balance as it may require weeks or months of time to develop such a comprehensive steam balance based on operating data, modeling, correlations, and experience. In the end of this endeavor when putting everything together, you may punch your fist to the sky and say 1 got it, baby ... 

The three vertices are the operating plant, the plant data, and the plant model. The plant produces a product. The data and their uncertainties provide the histoiy of plant operation. The model along with values of the model parameters can be used for troubleshooting, fault detection, design, and/or plant control. 

You should be able to estimate the quantities of material contained within a section from mechanical and operating data. You should also consider operating conditions, which should be available from the plant mass balance or from actual operating data. Simple hazard models can predict the size of vapor clouds, radiation hazards from fires, and explosion over-pressures. Such models are available from a number of sources. 

In the present study, we propose a tuning method for PID controllers and apply the method to control the PBL process in LG chemicals Co. located in Yeochun. In the tuning method proposed in the present work, we first find the approximated process model after each batch by a closed-loop Identification method using operating data and then compute optimum tuning parameters of PID controllers based on GA (Genetic Algorithm) method. 

The overall system that we will analyze comprises the unbleached Kraft pulp line, chemicals and energy recovery zones of a specific paper mill (Melville and Williams, 1977). We will employ a somewhat simplified but still realistic representation of the plant, originally developed in a series of research projects at Purdue University (Adler and Goodson, 1972 Foster et al., 1973 Melville and Williams, 1977). The records of simulated operation data, used to support the application of our learning architecture, were generated by a reimplementation, with only minor changes, of steady-state models (for each individual module and the system as a... 

In this chapter we revisited an old problem, namely, exploring the information provided by a set of (x, y) operation data records and learn from it how to improve the behavior of the performance variable, y. Although some of the ideas and methodologies presented can be applied to other types of situations, we defined as our primary target an analysis at the supervisory control level of (x, y) data, generated by systems that cannot be described effectively through first-principles models, and whose performance depends to a large extent on quality-related issues and measurements. 

There are many other S02 simulation using the models described in Tables IX and X or their further simplifications (Boreskov and Matros, 1984 Matros, 1989 Bunimovich etal., 1990 Sapundzhiev etal., 1990 Snyder and Subramanian, 1993 Xiao and Yuan, 1994, 1996 Zhang et al. 1995 Wu et al., 1996). These simulations are capable of reproducing operating data as demonstrated in Table VIII and in the preceding discussion. They have been useful in understanding the application of periodic flow reversal to S02 oxidation, as we shall see. 

When fitting new data to the model, it is often acceptable to assume the coefficient a to be zero. This simplifies the equations for the model. More importantly, it also simplifies data regression to fit manufacturers data or operating data to the model. After substituting a to be... 

The correlating parameters for variation power and heat rate with ambient temperature are specific to a particular gas turbine model. The parameters in the model can be determined from detailed simulation of the gas turbine or by fitting operating data from existing gas turbines, under different operating conditions. 

Some recent applications have benefited from advances in computing and computational techniques. Steady-state simulation is being used off-line for process analysis, design, and retrofit process simulators can model flow sheets with up to about a million equations by employing nested procedures. Other applications have resulted in great economic benefits these include on-line real-time optimization models for data reconciliation and parameter estimation followed by optimal adjustment of operating conditions. Models of up to 500,000 variables have been used on a refinery-wide basis. 

The second step that will be needed to ensure ready application of air quality models is largely a question of packaging and presentation. User-oriented documentation will be needed at data-processing centers for personnel who may not be specialists in chemistry, mathematics, or meteorology. Experience has shown that the user desires to operate the model in his own data center and wishes to understand enough about the model structure to explain it to others in his field. Models that can-... 

A neural-network-based simulator can overcome the above complications because the network does not rely on exact deterministic models (i.e., based on the physics and chemistry of the system) to describe a process. Rather, artificia] neural networks assimilate operating data from an industrial process and learn about the complex relationships existing within the process, even when the input-output information is noisy and imprecise. This ability makes the neural-network concept well suited for modeling complex refinery operations. For a detailed review and introductory material on artificial neural networks, we refer readers to Himmelblau (2008), Kay and Titterington (2000), Baughman and Liu (1995), and Bulsari (1995). We will consider in this section the modeling of the FCC process to illustrate the modeling of refinery operations via artificial neural networks. 

Working range for special models or special operating data... 

Here, n, v, and p represent a specific growth rate, a specific substrate consumption rate, and a specific product formation rate, respectively. and are the mean values of data used for regression analysis and a, bp and C are the coefficients in the regression models that are determined based on selected operating data in a database. This model was linked with the dynamic programming method and successfully applied to the simulation and onhne optimization of glutamic acid production and Baker s yeast production. 

The model optimized with regard to numerical and physico-chemical parameters has been tested with experimental data from a pilot plant, and used to evaluate industrial operation data. Here, a good agreement between experimental and simulated values is established, both for the gas-phase concentration of CO2 (Fig. 9.19) and for the temperature in the liquid phase (Fig. 9.20). 

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